Harnessing Generative AI in Private Equity Bain & Company

Lower down the value chain; field technicians stand to benefit from context-based information that can assist them in completing their work faster and more accurately. There are plenty of opportunities for high-tech companies to apply generative AI to transform every step of their value chain. High-tech supply chains are very complex, and the slightest disruptions can have impacts measured in months. Supply chain resilience can be improved by using LLMs (large language models) for due diligence and end-to-end contract management as well as by giving stakeholders the visibility they need to react much faster. “This includes increasing the level of productivity through direct efficiency gains as well as accelerating the rate of innovation and future productivity growth,” Korinek says.

In early 2023, I became one of the first people to get a generative AI assistant at work–and then I had to figure out how to use it. But my biggest lesson along the way is that working with artificial intelligence isn’t like onboarding a new assistant. Since Apple introduced the iPhone in 2007, Google has been a critical contributor to the device’s success. It initially provided Google Maps for navigation and the default search engine on the iPhone’s Safari browser, now a lucrative agreement for which Google pays Apple more than $18 billion a year.

The manufacturers of these chips must keep these factors in mind during their research and development phases so the designed chips are relevant in the market, ensuring a positive impact on the economic landscape. The rapidly evolving technological landscape of the AI chip industry has promoted an era of innovation among competitors. It has led to the development of several types of chips that are available for use today. Within the economic potential of generative AI in the chip industry, Microsoft describes its goal to tailor and produce everything ‘from silicon to service‘ to meet the AI demands of the evolving industry. Microsoft holds a unique position where it is one of the leading consumers of the AI chip industry while aiming to become a potential contributor. Since the generative AI projects rely on chips from companies like NVIDIA, Microsoft has shown potential to create custom AI chips.

Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs. Our analysis did not account for the increase in application quality and the resulting boost in productivity that generative AI could bring by improving code or enhancing IT architecture—which can improve productivity across the IT value chain. However, the quality of IT architecture still largely depends on software architects, rather than on initial drafts that generative AI’s current capabilities allow it to produce.

• EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity.
• Generative AI possesses the power to create human-like content instantaneously, unlocking new levels of productivity across various sectors of our economy.
• Each factor adds to the competitiveness of the market, fostering growth and innovation.
• In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator.
• It has led to the development of several types of chips that are available for use today.
• Generative AI has the potential to automate certain tasks, displacing some workers, and it can also create new jobs and industries.

In the entertainment industry, gen AI creates personalized recommendations for movies, TV shows, and music based on individual preferences. This technology can foster the same efficiency and accuracy that it does in other industries, making it a potential cost-saver for media companies. In the healthcare industry, gen AI is used to analyze medical images and assist doctors in making diagnoses. According to a report by the World Health Organization (WHO), up to 50% of all medical errors in primary care are administrative errors. Gen AI has potential to increase accuracy, but the technology also comes with vulnerabilities, as its trustworthiness depends heavily on the quality of training datasets, according to the World Economic Forum. McKinsey’s report is one of the few so far to quantify the long-term impact of generative A.I.

Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. LPUs are designed to handle specific high-performance generative AI tasks like inferencing LLMs or generating images.

McKinsey & Company

That could generate margin improvement of 10% to 15% in the midterm as revenue expanded—enough to give the buyer an added layer of conviction that the target would be able to justify its multiple. The fast-paced technological world of today is marked by developments in generative AI. According to Statista Market Insights, the generative AI market size is predicted to reach $70 billion in 2030. Among these several chip designs available and under development, the choice within the market relies on multiple factors.

Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. After a decade-long run as the world’s most valuable public company, it was dethroned this year by Microsoft, which has aggressively pursued A.I.

Economic Impact of Gen AI: Expert Opinion

While this could lead to job displacement, the report also noted that just because AI could automate a job doesn’t necessarily mean that it will, as cost, regulations, and social acceptance can also be limiting factors. With its ability to leverage vast amounts of data and predict outcomes, AI can significantly improve decision making, optimize production, enhance product quality, and reduce waste. Properly managing the workforce changes posed by generative AI could raise the global GDP by 7% in just 10 years.

This analysis may not fully account for additional revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue. Also, the time saved by sales representatives due to generative AI’s capabilities could be invested in higher-quality customer interactions, resulting in increased sales success. Following are four examples of how generative AI could produce operational benefits in a handful of use cases across the business functions that could deliver a majority of the potential value we identified in our analysis of 63 generative AI use cases. In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy.

But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. An analysis of various knowledge-work tasks across the company suggested that several departments could do more with less by automating certain activities and using AI to speed up others.

Primarily, the choice is dictated by the needs of the AI application and its developmental stage. While a GPU might be ideal for early-stage processing, ASICs are more useful for later stages. Its programmability makes them versatile as the chips can be reprogrammed after each specific use. Moreover, these chips are also expensive, incurring high costs to the users, making their adoption within the industry limited.

There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. It is important to navigate the impact and economic potential of generative AI in the chip design industry as it maps out the technological progress and innovation in the digital world.

But AI tools allow you to scrape every review ever posted to the Internet within minutes, organize the comments meaningfully, and then generate a clear, analytical report. Due diligence teams can also use generative AI to get a more complete picture of a target company’s prospects. Powerful tools are rapidly emerging to scan reams of data in a fraction of the time it would take a human to do the same job.

Pharma companies typically spend approximately 20 percent of revenues on R&D,1Research and development in the pharmaceutical industry, Congressional Budget Office, April 2021. With this level of spending and timeline, improving the speed and quality of R&D can generate substantial value. For example, lead identification—a https://chat.openai.com/ step in the drug discovery process in which researchers identify a molecule that would best address the target for a potential new drug—can take several months even with “traditional” deep learning techniques. Foundation models and generative AI can enable organizations to complete this step in a matter of weeks.

Gauging the productivity potential of GenAI

We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data. Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications. The result is a short list of indications that have a better probability of success in clinical trials because they can be more accurately matched to appropriate patient groups.

Adoption is also likely to be faster in developed countries, where wages are higher and thus the economic feasibility of adopting automation occurs earlier. Even if the potential for technology to automate a particular work activity is high, the costs required to do so have to be compared with the cost of human wages. In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9). Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023.

The million-dollar question was how the company could accelerate those efforts using generative AI. The answer was setting up an “MVP accelerator” to identify generative AI applications, develop the business case, build a minimum viable product (MVP), and test and learn to refine a solution. Scattershot initiatives will not drop any money to the bottom line, but a series of use cases targeted at a specific role or process very well might. While starting now with a test-and-learn mindset is critical, it’s as important to prioritize investment against the initiatives most likely to deliver the highest value.

And firms such as Microsoft and Google are embedding generative ai into their office software, meaning that anyone opening up a document or a spreadsheet will soon be able to make use of the tools. The insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. We develop outstanding leaders who team to deliver on our promises to all of our stakeholders. In so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. Optimizing inventory management and recommending products to customers based on their purchase history and browsing behavior is only part of the value of gen AI in the retail industry. As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work.

The report arrives as Silicon Valley has been gripped by a fervor over generative A.I. Tools like ChatGPT and Google’s Bard, with tech companies and venture capitalists investing billions of dollars in the technology. “Generative artificial intelligence” is set to add up to $4.4 trillion of value to the global economy annually, according to a report from McKinsey Global Institute, in what is one of the rosier predictions about the economic effects of the rapidly evolving technology.

Productivity growth is the main long-term propeller of economic growth and living standards, but growth has slowed in recent decades and remains on a subdued trend, even as GenAI adoption continues to quicken. While the timeline of when this labor productivity boom would occur the economic potential of generative ai is relatively uncertain, there is no question that the economic impacts will be significant. If generative AI lives up to its foreseen capabilities in the coming decades, we could see a technological revolution as impactful as the automobile and the personal computer.

Going through this exercise at Multiversity and other companies in its portfolio, meantime, has turned into a master class in generative AI for CVC. Scanning the portfolio is making the firm smarter and enabling it to be more responsive when it comes to deploying these technologies. Accenture’s High Tech global lead, helping clients with growth strategy, reinvent their business and optimize supply chain.

In assessing the potential economic impact of GenAI from a productivity perspective, it is worthwhile to consider the TFP dynamics observed during the ICT revolution. Looking across major economies, a GenAI-driven productivity upswing could also make a substantial contribution to the global economy. We estimate that the lift to global GDP from stronger productivity could total $1.2t to $2.4t over the next decade. Numerous case studies and reports have pointed to AI’s impact on various industries, the economy, and the workforce. For example, generative AI can help retailers with inventory management and customer service, both cost concerns for store owners.

As GenAI technologies gain traction, labor productivity will likely rise through direct labor efficiency gains but also through the enhancement of organizations and business processes. Any productivity increase that is not the result of changes in capital or labor inputs is measured as total factor productivity (TFP). Our analysis finds that generative AI could have a significant impact on the pharmaceutical and medical-product industries—from 2.6 to 4.5 percent of annual revenues across the pharmaceutical and medical-product industries, or $60 billion to $110 billion annually. This big potential reflects the resource-intensive process of discovering new drug compounds.

Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. Thus, the impact of generative AI is expected to grow in the future, subsequently leading to the growth of AI chip designs. The advent of generative AI represents a significant leap forward in the development of artificial intelligence. As businesses race to embrace and integrate this technology, comprehending its potential to contribute value to the economy and society becomes pivotal in making informed decisions.

Chatbots are prone to “hallucinations”, or making up things that sound dangerously plausible. And writers, artists, photographers and publishers are challenging AI models’ use of their data in court. Some businesses are wary of being exposed to legal risk by making use of the models, or the reputational risk of taking hallucinations seriously. JPMorgan Chase, a bank, has banned the use of ChatGPT, though it is experimenting with AI in other areas. In these major domains, GenAI stands not just as a tool but as a transformative force, reshaping the way tasks are approached and executed, which can lead to unprecedented levels of efficiency and innovation.

Generative AI could propel higher productivity growth

Traditional models have been trained on smaller, specialized datasets to serve a specific purpose (e.g., analyze previous machine maintenance patterns to predict when servicing is necessary). Generative AI models are trained on large databases, such as the entire publicly available internet, and so can serve a much wider range and versatility of use cases. The tools — some of which can also generate images and video, and carry on a conversation — have started a debate over how they will affect jobs and the world economy. Will displace people from their work, while others have said the tools can augment individual productivity. Has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities,” the report said. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased.

Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents.

Across the banking industry, for example, the technology could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion a year. The talks are preliminary and the exact scope of a potential deal hasn’t been defined, three people with knowledge of the discussions said. Companies, one of these people said, as it looks to tap into the power of a large language model capable of analyzing vast amounts of data and generating text on its own.

Broad productivity gains across sectors and occupations

In today’s rapidly evolving technological world, the economic potential of generative AI and other cutting-edge industrial developments is more pronounced than ever before. This is the third installment of the EY-Parthenon macroeconomic article series on the economic impact of AI. The series aims to provide insights on the economic potential of generative AI (GenAI), including new developments and actionable insights to arm companies’ decision makers. The third article in this series discusses future productivity effects of GenAI by examining multiple scenarios, historical lessons and recent case studies. The effect of technological innovation on the economy is typically measured indirectly as economic output growth that cannot be accounted for by changes in capital or labor inputs used in the production process. It’s generally captured in TFP but is often measured as greater labor productivity growth.

Our analysis builds on the scenarios developed in the previous chapter on capital investment. We then estimated the growth effects of these productivity scenarios on long-run GDP growth using a growth accounting approach such as Fernald (2014). By executing and automating complex cognitive tasks that previously only humans could perform, GenAI has the potential to enhance workers’ efficiency, accelerate capital deepening and unlock substantial productivity gains across the economy. Looking back at history, TFP was a driving force behind the acceleration in US labor productivity growth that took place during the ICT revolution of the late 1990s. Beginning in the mid-1990s, output per hour began to grow rapidly, reversing the productivity growth slowdown of the 1980s. After averaging 1.4% annually from 1973 to 1990, labor productivity growth accelerated to 2.2% between 1990 and 2000 and 2.7% between 2000 and 2007.

In the next installment of this series, we will examine the labor-augmenting capabilities of GenAI across sectors and occupations in greater detail. A study by the World Economic Forum found that adopting AI could lead to a net increase in jobs in some industries, Chat PG particularly those that require higher levels of education and skills. However, the report also warned that the benefits of AI could be unevenly distributed, with some workers and regions experiencing more significant job displacement than others.

In the financial industry, AI algorithms detect fraud and identify investment opportunities. Generative AI has shown the potential to automate routine tasks, enhance risk mitigation, and optimize financial operations. AI has been driving value for businesses since the early 2000s; however, the majority of AI models have been discriminative, not generative.

• And, as we saw in the first installment of our article series, it could also take time for the productivity benefits of GenAI to materialize.
• Within two months 100m users were posing all sorts of entertaining queries (“Write me a rap song using references to SpongeBob SquarePants”).
• In the banking industry, generative AI has the potential to improve on efficiencies already delivered by artificial intelligence by taking on lower-value tasks in risk management, such as required reporting, monitoring regulatory developments, and collecting data.
• This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI.

A report released by Goldman Sachs in March predicts that generative AI could, within a decade, raise annual global GDP by 7 per cent, which translates to a roughly $7 trillion increase. The report modelled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities” that make up those occupations across the world economy. It also states that beyond its potential value in specific use cases, generative AI has the capacity to transform internal knowledge management systems, thereby driving value across the entire organization. Interestingly, the report also outline how generative AI use cases will have different impacts on business functions across industries.

Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks.

Some of its capabilities include massively parallel processing and handling large matrix multiplications. Unlike other manufacturers focused on developing the new chips for businesses, Google AI plays a more collaborative role. It partners with these manufacturers to contribute through research and model development.

More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. The growth of e-commerce also elevates the importance of effective consumer interactions. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity.

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development.

Apple is in discussions with Google about using the search giant’s generative artificial intelligence model called Gemini for its next iPhone, as the company races to embrace a technology that has upended the tech industry. Generative AI tools are ideal for scanning massive pools of data for insights using the firm’s preferred screening criteria. Currently, this fund’s professionals tend to look at 10 deals to find 1 worth investigating further. Armed with a set of seven key criteria linked to the fund’s strategy, they spend a full day on most “looks,” or half a day if they’re lucky. Generative AI can not only help produce the initial list faster but can also bring down the screening time per company from a day to an hour. This makes team members significantly more productive and frees them up to focus on the more qualitative work involved in analyzing the potential gems that make it through the funnel.

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. You can foun additiona information about ai customer service and artificial intelligence and NLP. This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases.

An ecosystem approach can deliver great results—just look at the semiconductor industry and its fabless model or how software and platforms companies have leveraged the investments of the telco industry to thrive. Because building out AI offerings is a huge undertaking—it’s costly and requires extensive infrastructure—I’d strongly recommend high-tech businesses create an intentional ecosystem that leverages each stakeholder’s strengths and hedges against risks. The magnitude of the productivity boost from GenAI will depend on the speed of its diffusion across organizations and industries. While GenAI has already spawned many innovations, it has yet to show a visible and meaningful boost in the aggregate productivity data. As we highlighted in our first article, the productivity boost from GenAI will likely occur with a lag as there has generally been a long delay between the inception of paradigm-shifting technologies and their diffusion across the economy and society.